Ads Recommendation System

This project implements an AI-powered advertisement recommendation system that suggests relevant ads based on user preferences. The system uses advanced embedding techniques and similarity search to provide personalized ad recommendations.

Features

• Load and process advertisement data
• Generate embeddings for ad content
• Similarity-based recommendation engine
• Interactive Streamlit web interface
• Qdrant vector database integration

Setup

1. Clone the repository:

git clone https://github.com/yourusername/ads-recommendation-system.git
cd ads-recommendation-system

2. Install dependencies:

pip install -r requirements.txt

3. Run the Streamlit app:

streamlit run main.py

Project Structure

ads-recommendation-system/
│
├── data/                      # Dataset directory
├── src/                      # Source code
├── requirements.txt          # Project dependencies
├── README.md                 # Project documentation
├── .gitignore               # Git ignore rules
└── main.py                  # Main Streamlit application

Usage

1. The system loads advertisement data from the provided JSON dataset
2. Users can input their preferences or interests
3. The system returns relevant ad recommendations based on the input

License

MIT License